Efficient Data Representation Combining with ELM and GNMF

  • Zhiyong Zeng
  • YunLiang Jiang
  • Yong Liu
  • Weicong Liu
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 16)


Nonnegative Matrix Factorization (NMF) is a powerful data representation method, which has been applied in many applications such as dimension reduction, data clustering etc. As the process of NMF needs huge computation cost, especially when the dimensional of data is large. Thus a ELM feature mapping based NMF is proposed [1], which combined Extreme Learning Machine (ELM) feature mapping with NMF (EFM NMF), can reduce the computational of NMF. However, the random parameter generating based ELM feature mapping is nonlinear. And this will lower the representation ability of the subspace generated by NMF without sufficiently constrains. In order to solve this problem, this chapter propose a novel method named Extreme Learning Machine feature mapping based graph regulated NMF (EFM GNMF), which combines ELM feature mapping with Graph Regularized Nonnegative Matrix Factorization (GNMF). Experiments on the COIL20 image library, the CMU PIE face database and TDT2 corpus show the efficiency of the proposed method.


Extreme learning machine ELM feature mapping Nonnegative matrix factorization Graph regularized nonnegative matrix factorization EFM NMF EFM GNMF Data representation 



We want to thank Dr. Huang Guangbin from NTU and Dr. Jin Xin from Chinese Academy of Sciences. They provide us with some codes and details of Extreme Learning Machine. This work was supported by the National Natural Science Foundation Project of China (61173123) and the Natural Science Foundation Project of Zhejiang Province (LR13F030003).


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Hangzhou Dianzi UniversityHangzhouChina
  2. 2.Huzhou Teachers CollegeHuzhouChina
  3. 3.Zhejiang UniversityHangzhouChina

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